MeLOn
example_training_of_ANN.py File Reference

Namespaces

 example_training_of_ANN
 

Variables

string example_training_of_ANN.problem_name = "peaks"
 LOAD DATA ############################ enter data set information. More...
 
string example_training_of_ANN.filename_data = "./data/peaks.csv"
 
int example_training_of_ANN.input_dim = 2
 
int example_training_of_ANN.output_dim = 1
 
bool example_training_of_ANN.scaleInput = True
 
bool example_training_of_ANN.normalizeOutput = True
 
 example_training_of_ANN.data = np.loadtxt(open(filename_data, "rb"), delimiter=",")
 
 example_training_of_ANN.X = data[:, :-output_dim]
 
 example_training_of_ANN.y = data[:, input_dim:]
 
 example_training_of_ANN.X_norm = utils.scale(X, scaleInput)
 
 example_training_of_ANN.y_norm = utils.normalize(y, normalizeOutput)
 
 example_training_of_ANN.x_train
 
 example_training_of_ANN.x_val
 
 example_training_of_ANN.y_train
 
 example_training_of_ANN.y_val
 
 example_training_of_ANN.test_size
 
 example_training_of_ANN.n_train = x_train.shape[0]
 
string example_training_of_ANN.output_folder = "./data/Output/"
 SET PARAMETERS ############################ output filename. More...
 
string example_training_of_ANN.filename_out = output_folder + problem_name
 
list example_training_of_ANN.network_layout = [10, 10]
 
string example_training_of_ANN.activation_function = 'relu'
 
string example_training_of_ANN.activation_function_out = 'linear'
 
float example_training_of_ANN.learning_rate = 0.001
 
 example_training_of_ANN.kernel_regularizer = tf.keras.regularizers.l2(l=0.0001)
 
string example_training_of_ANN.kernel_initializer = 'he_normal'
 
string example_training_of_ANN.optimizer = 'adam'
 
int example_training_of_ANN.epochs = 100
 
int example_training_of_ANN.batch_size = 128
 
int example_training_of_ANN.random_state = 1
 
 example_training_of_ANN.model = tf.keras.Sequential()
 BUILD MODEL ############################. More...
 
 example_training_of_ANN.loss
 
 example_training_of_ANN.metrics
 
 example_training_of_ANN.training_time = time.time()
 TRAINING ############################. More...
 
 example_training_of_ANN.history
 
 example_training_of_ANN.y_pred = model.predict(X_norm)
 SAVE MODEL ############################. More...